2022
DOI: 10.1111/2041-210x.13850
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Accelerating advances in landscape connectivity modelling with the ConScape library

Abstract: 1. Increasingly precise spatial data (e.g. high-resolution imagery from remote sensing) allow for improved representations of the landscape network for assessing the combined effects of habitat loss and connectivity declines on biodiversity. However, evaluating large landscape networks presents a major computational challenge both in terms of working memory and computation time. We present the ConScape (i.e. "connected landscapes") software library implemented in the high-performance open-source Julia language… Show more

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Cited by 12 publications
(31 citation statements)
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References 62 publications
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“…This is an increasingly prominent feature of statistical analyses. The type of software ranges from general statistical packages to which ecological models and data analyses can be conducted (such as inlabru Bachl et al, 2019or NIMBLE de Valpine et al, 2017, to specialised packages for very specific problems (Van Moorter et al, 2023).…”
Section: Softwarementioning
confidence: 99%
See 1 more Smart Citation
“…This is an increasingly prominent feature of statistical analyses. The type of software ranges from general statistical packages to which ecological models and data analyses can be conducted (such as inlabru Bachl et al, 2019or NIMBLE de Valpine et al, 2017, to specialised packages for very specific problems (Van Moorter et al, 2023).…”
Section: Softwarementioning
confidence: 99%
“…To address some of these challenges, Pawluczuk and Iskrzyński (2023) propose methods for visualising increasingly complex foodweb (and other network) structures by combining heatmaps, interactive and animated graphs. Alternatively, Van Moorter et al (2023) have developed the package ConScape (in Julia) which allows users to efficiently analyse and visualise landscape and habitat connectivity more simply. Further issues arise when attempting to analyse objects that contain multiple distinct (non‐independent) parts that make up the complete object (e.g.…”
Section: Broad Themesmentioning
confidence: 99%
“…Memory requirements can be reduced by iteratively applying the method to different source locations (Compton et al 2007;Cushman and Landguth 2012) or groups of them, and it would be possible to improve the useability of an iterative approach with parallelization. Other options for reducing the cost of calculations include reducing number of nodes considered (Van Moorter et al 2022) or simplifying neighbourhood geometry (Drielsma et al 2007(Drielsma et al , 2022. We have not pursued these options because we believe there are good theoretical reasons to prefer SAMC metrics over PARC/resistant kernel approaches, including explicit representation of mortality and movement that does not assume knowledge of least cost paths (Table 1).…”
Section: Omission Of Samc R and Parc Metricsmentioning
confidence: 99%
“…Patch-free raster-based moving window connectivity analysis methods are computationally intensive which has limited their use and utility thus far. Recent efforts to develop computationally efficient implementations of Circuitscape, Omniscape, and Randomized Shortest Paths in Julia have improved the useability of those methods (Hall et al 2021;Van Moorter et al 2022). Conceptually, SAMC metrics provide an intriguing alternative to circuit and resistant kernel metrics, but an implementation that requires construction of a full transition matrix (Marx et al 2020) is memory intensive and difficult or impossible to apply over large landscapes.…”
Section: Locke Et Al (2019) Have Proposed a "Three Global Conditions ...mentioning
confidence: 99%
“…However, we face a fundamental limitation to assessing metapopulation persistence empirically in landscapes supporting many populations [17]. As all patches have some non-zero influence on metapopulation capacity, to quantify metapopulation persistence would seem to require surveying or sampling all the habitat patches in a landscape for the relevant parameters, such as patch quality, pairwise patch distance and so on.…”
Section: Introductionmentioning
confidence: 99%